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In-flight experimentation and adjacent learning are the future of marketing

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In-flight experimentation and adjacent learning are the future of marketing

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Customer behaviors have always been shifting in response to Industry revolution and changing needs, they have now accelerated like never before due to social network explosion and pandemic events like covid19.

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"A good design adds value faster than it adds costs!" - Is my campaign design future ready? Are they adding value or just more cost?
These have been key challenges faced by marketeers across the globe for a while now and tectonic shifts in customer behaviors have made it more difficult to find answers to these. Although, customer behaviors have always been shifting in response to Industry revolution and changing needs, they have now accelerated like never-before due to social network explosion and pandemic events like covid19.
Do you Know?
"Rapid Experimentation: Digital world is exploding and there's a whole lot we don't know yet!"

Experimentation has been accepted as a key to future-proof the business for its ability to dip in latent needs of customers which they are yet to express.

While experimentation is not a new concept, the need of the hour is to perform these experiments Quicker, At Scale and in an Ongoing Manner. And to create a feedback mechanism where these learning can be used to course-correct while campaigns are still In-Flight.
A Hyper Sense and Response Framework.
The concept of Pilot Phase is no more relevant as experimental learning needs to be a continuous process. Conventional methods like A/B testing and Test and Control are not practical as they fail to experiment at scale. Multivariate testing brings in large scale experimentation but slows our Time to Market and is not continuous and self-learning. Thus, the conventional methods fail to add significant value to the ongoing campaigns.

A Smart Campaign Design with a Large-Scale Micro Experimentation mandate will be instrumental in exploring the new opportunities in a scalable manner. And these experiments when connected with ongoing campaigns with a Feedback Loop, will help course-correct campaigns to ensure they are on track for fulfilling their goals.
Large Scale Micro Experimentation - Applied to upcoming Back to School Campaign For a Retailer, this year's Back to College season will bring in demand for new set of products as many school and college students will most likely be staying indoors. E.g. Demand for Clothes, Shoes, School Supplies, Electronic and Computer Related Equipment will most likely be replaced. But, if these regular Back to College categories will be replaced by Indoors board games, Kindles, Gardening Tools, Musical Instruments is an unknown and will vary with customer. Large Scale and Continuous Experimentations are needed to capitalize on this opportunity.
"Design campaigns based on latest trends rather than past benchmarks"

Fast paced changes in the world of ecommerce and evolving customer behavior, makes benchmarks based on last year's data irrelevant. It'll be a crime to design Back to School campaign for 2020 based on learning from 2019. What's more relevant is to learn from campaigns that are like Back to School campaign and have been deployed in near past.

How do you achieve that? A smart campaign design should be based on Campaign Feature Analysis and Cross Cluster Learning. Simply put, de-constructing a campaign into features, finding other campaigns which have similar features and identifying campaigns which are look-alike.

Data Recency and Data Relevance is the key. A right balance of these two with a proper weight assignment can help find and learn winning features from similar campaigns. We call it Adjacent Learning.
Adjacent Learning - Applied to upcoming Back to School Campaign A Back to School Campaign can be deconstructed into features -Target Customer, Offer, It's association with holiday period among other features. We can then identify campaigns that score similar across these features and zero down on those which are Look Alike and were also executed in Near Past. If it was a Long Weekend campaign, which was exposed to Students, we can learn wining features and key performing categories from this campaign as we design our Back to School campaign for 2020.
"Rapid, large scale experimentation for inflight course corrects - let's make it happen"

Many companies have been benefited by experimentation and testing in past. Booking.com runs more than 1,000 rigorous tests simultaneously and more than 25,000 tests a year. Microsoft's Bing unit make dozens of monthly experiments, which collectively have boosted revenue per search by 10% to 25% a year. Non tech companies like FedEx, State Farm, and H&M-have also embraced online testing, using it to identify the best digital touchpoints, design choices, discounts, and product recommendations.

However, to future proof your businesses, it is time to go one step further with the power of AI and realize the real potential of experimentation. We suggest an AI driven platform to design a holistic solution which

Keeps pace with evolving customer behavior

Utilizes knowledge from ongoing campaigns and the ones from near past

Experiment to explore new opportunities

A platform where Experimentation is not an after-thought, but it is THE thought

Let's you run large number of experiments which are self-learning and self-calibrating over time

Reduces time to market associated with new campaigns by responsive learning loop

Enables smart designing of marketing creative

And optimizes campaigns on the fly

"Now is the time to Act! Connect with us to explore how Tredence can help you get ready for the Future"

2.Business 2 Community: 3.Voicebot.ai: 4.Hackernoon: 5.COVID-19 Benchmarks: 6.Micro-Moments: 1.Agility PR: https://www.agilitypr.com/pr-news/public-relations/consumer-behavior-in-the-coronavirus-crisis-new-insights-on-the-impact-of-covid-19/ https://www.business2community.com/consumer-marketing/6-important-stats-consumer-behavior-changed-digital-age-01489789
https://voicebot.ai/2018/04/17/one-in-four-online-shoppers-use-voice-assistants-on-mobile-to-shop/
https://hackernoon.com/outstanding-ar-experiences-in-the-retail-sector-2963f674bca2
https://blog.hubspot.com/marketing/covid-19-benchmark-data
Your Guide to Winning the Shift to Mobile

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In-flight experimentation and adjacent learning are the future of marketing

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"A good design adds value faster than it adds costs!" - Is my campaign design future ready? Are they adding value or just more cost? These have been key challenges faced by marketeers across the globe for a while now and tectonic shifts in customer behaviors have made it more difficult to find answers to these.

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 The role of artificial intelligence in healthcare - Healthem.ai

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 The role of artificial intelligence in healthcare - Healthem.ai

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Artificial Intelligence (AI) has infiltrated and influenced our lives to such an extent healthcare systems will be able to forecast if a person will develop a chronic illness and take preventive actions to prevent it.

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Do you have a fitness band or a smartwatch, or have any diet or exercise applications installed on your phone? If yes, you can see how Artificial Intelligence (AI) has invaded and influenced our lives.

By 2025, it is anticipated that there will be close to one trillion connected devices worldwide, owing to the increasing penetration and popularity of AI-enabled devices. These devices provide an avalanche of data that helps carriers better understand their customers, resulting in new products, targeted pricing, and real-time service delivery. Similarly, AI, machine learning (ML), and the Internet of Things (IoT) can personalize patient therapy by anticipating their needs based on their behavior. By 2030, AI will have access to a wide range of data sources, recognize disease trends, and pinpoint medication and care, according to the World Economic Forum. Furthermore, healthcare systems will be able to forecast whether a person will develop a chronic condition and will offer proactive preventative remedies.
AI in healthcare
Health care is a high-risk industry. Fortunately, AI can improve or speed up many of its systems and procedures, including:

Pre-screening patients

Intake and triage of patients

Medical imaging and diagnosis

Preventive care based on data

Drug discovery

Enhanced care

By improving workflows and operations, assisting medical and non-medical workers with repetitive activities, assisting users in gaining faster answers to queries, and generating innovative treatments and therapies, AI in healthcare can benefit patients, payers, researchers, and doctors.
AI-led transformation in health insurance
Insurance companies in the United States are already utilizing healthcare data analytics to provide customized services based on risk profiles and identify qualifying patients for wellness programs to be engaged as soon as possible. Because of this transformation, insurance will change from spot and repair' to 'foresee and avoid,' affecting every industry area. As brokers, clients, financial intermediaries, insurers, and suppliers grow more competent at harnessing advanced technology to improve decision-making, customer experience, productivity, and cost-cutting, the rate of change will ramp up. As a result, AI in healthcare would become one of the fastest-growing investments in the sector. According to Accenture research, the health AI market was worth $600 million in 2014 and is anticipated to increase to $150 billion by 2026.
AI case examples
In action, AI can change the face of health care, save lives, and support better global health.

Livongo Health is a health firm that offers blood glucose meters, scales, and other products while collecting data from users and storing it in a large database. The organization uses this information to send customized messages to the right customers at the right time to interact with them one-on-one. They also use big data to spot trends and determine which treatments are effective for each individual.

According to a Mayo Clinic study, utilizing AI in electrocardiograms can improve clinicians' ability to detect early indicators of asymptomatic left ventricular failure, a well-known risk factor for heart failure and other heart-related disorders.

A team of academics and scientists from Moorfields Eye Hospital in London and University College London (UCL) demonstrated that AI-powered software could detect up to 50 eye illnesses with the same accuracy as a doctor by quickly glancing through images.
AI's role in telemedicine and pharma
Telemedicine has emerged as the new healthcare paradigm since AI has proven to be a powerful tool for the future of care delivery. With robots, AI in telemedicine can help with intelligent diagnosis and remote patient monitoring, reduce hospital visits, and give better care to older patients. In turn, AI-enabled robots can remind patients to take their meds, monitor their vital signs, and alert others in the event of an emergency. Further, AI in telemedicine can help make health care more accessible and affordable in many developing nations with a skewed doctor-patient ratio.

The McKinsey Global Institute estimates that AI and machine learning in the pharmaceutical industry may produce $100 billion in revenue per year for the US healthcare system. Artificial intelligence (AI) has the potential to transform pharmaceutical discovery by speeding up research and development, decreasing drug prices, and boosting the likelihood of FDA approval. AAI and machine learning algorithms can identify substances that failed in clinical trials and predict how they might use them to treat various diseases. Also, AI can impact quality control, material waste reduction, factory reuse, and predictive maintenance. In addition, machine learning can assist in forecasting and reducing over- and under-demand and managing supply chain difficulties and production line failures.
AI's role in Proactive Care Management
AI has emerged as a powerful tool for the future of care delivery, with proactive care management emerging as the new healthcare paradigm. The challenges with traditional sensor technologies, such as remote monitoring patient devices, are the lack of proactive monitoring to prevent potential incidents or predict complications of a specific chronic condition. Also, traditional risk tools only evaluate current risks, which are factored in as high risks for the future. Moreover, this model fails to identify healthy patients who can turn into high-risk eventually. The number of patients requiring emergency admission, the best treatment options available, and the cost of care, among other things, can be predicted using AI/ML by analyzing patient behavior patterns, vitals, and crucial health indicators. On the other hand, traditional sensors will only alert you after an event has occurred, such as a brain stroke or cardiac arrest.

AI's primary goal is to improve human capabilities, resulting in improved patient outcomes and lower costs. AI can aid clinicians with tasks and healthcare data analytics, resulting in improved efficiencies and care. For example, a patient with a specific condition may only have a rudimentary awareness of how the illness advances. Several underlying factors could be contributing to the problem's escalation. As a result, clinicians today need technology to create a 360-degree care plan that addresses all disorders at once rather than one at a time.

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The role of artificial intelligence in healthcare - Healthem.ai

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Do you have a fitness band or a smartwatch, or have any diet or exercise applications installed on your phone? If yes, you can see how Artificial Intelligence (AI) has invaded and influenced our lives. By 2025, it is anticipated that there will be close to one trillion connected devices worldwide, owing to the increasing penetration and popularity of AI-enabled devices.

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Machine Learning Operations | MLOps Platform (ML Works)

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Machine Learning Operations | MLOps Platform (ML Works)

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Leverage Tredence’s Machine Learning Operations (MLOps) platform, ML Works, to revitalize enterprise ML ecosystem, fast track model management and reduce outages.

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Scale thousands of machine learning models, reduce outages and simplify model monitoring

Across industries, companies are struggling with AI projects that are too difficult to manage, either because teams have to start working from the ground up or because their efforts have to be scaled manually, taking up too much time and effort to see the light of day. For AI to make a significant contribution to the business, it must be industrialized - scaled across the organization and embedded into core processes and workflows.

Only highly reliable AI models can help in achieving such scale, when powered by ML Ops which provides a best-in-class framework for the AI life cycle. Leverage our ML Works solution to not only manage ML Ops successfully but also industrialize it. Our automated workflows and pre-built accelerators help you track model degradation, manage code workflow, and fast track model Ops management across the enterprise. Scale thousands of machine learning models, reduce outages and simplify model monitoring with ML Works.

Here's how we've helped our customers win at the last mile

Autonomous ModelOps management for a CPG leader to increase trade promotion effectiveness

See how we helped a Fortune 500 CPG leader tackle long production downtimes and erroneous outcomes that affected their trade promotion program with effective model management.

We streamlined ML pipeline monitoring across 66,000 models, optimized drift detection and explainable AI and delivered persona-based insights for each function, so the customer could derive multiple benefits with Model Ops.

Increased quarterly sales by 3%

Reduced pipeline downtime from 4-6 days to 4 hours

Effectively used $300M trade funds with reduced errors

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Machine Learning Operations | MLOps Platform (ML Works)

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Across industries, companies are struggling with AI projects that are too difficult to manage, either because teams have to start working from the ground up or because their efforts have to be scaled manually, taking up too much time and effort to see the light of day.

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Retail Predictive Analytics | Retail Data Analytics Company - Tredence

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Retail Predictive Analytics | Retail Data Analytics Company - Tredence

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Leverage custom full-stack, white box AI/ML driven retail predictive analytics and retail customer analytics solutions by Tredence to turn your data into insights, drive top-line growth and bottom-line savings.

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Turn your mountains of enterprise data into actionable insights that drive top-line growth and bottom-line savings

The pandemic is a watershed moment for the retail digital transformation, boosting retailers' appetite for new revenue opportunities. Many have turned to enterprise data for its enormous potential to drive top-line growth and bottom-line savings.

The challenge? Retailers are rich in data but starving for insights. They are struggling to harness the full potential of their enterprise data and drive value:

Siloed data

locked in legacy systems provides an incomplete customer and operations view.

Insights can't keep up with consumers

as dated analytic approaches and complex solutions are too slow to generate real-time recommendations.

Black box solutions turn up empty

as complex and inflexible solutions require too many resources for implementation and interpretation to drive value.

Retailers are paralyzed by the overabundance of data

and require true AI/ML to unlock insights.

Tredence's end-to-end offerings unlock growth and create new revenue streams

We build data strategies, modernize your platforms and help unlock value from your enterprise data leveraging our suite of 30+ retail accelerators - from supply chain, procurement and merchandising to marketing, personalization, customer analytics and price optimization.

Powering omni-channel customer visibility for a retailer serving 100+ million households

See how Tredence's customer data platform, Customer Cosmos,

Re-engaged 25 million at-risk customers

Increased customer lifetime value by $230 per household

Boosted conversion by 5 points for one of the world's largest retailers serving over 100 million households

$4.8M cost savings per year from optimized storage and processing

14% increase in addressable customer data and visibility

5X improvement in query and ML segmentation processing time

Improving demand forecast accuracy by 600 BPS for a Top 10 retailer using machine learning

Discover how we helped a global retail giant streamline their forecasting, create a fact-based approach and optimize their inventory through a better understanding of real-time demand.

Using machine learning to diagnose performance drivers and optimize seasonal inventory, we were able to deliver a range of benefits.

600 basis point accuracy improvement

50% reduced planning time, which improved efficiency

6% reduction in inventory costs, enhancing ROI

Enable last-mile adoption of insights

Discover how our curated insights can help you drive last-mile impact.

Tredence Partners with Databricks to Co-develop a Retail On-Shelf Availability (OSA) Solution Accelerator

Happier holidays for Retailers: 5 keys to managing the seasonal supply chain crisis with analytics and data science

Why becoming data-driven might not be enough

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Retail Predictive Analytics | Retail Data Analytics Company - Tredence

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The pandemic is a watershed moment for the retail digital transformation, boosting retailers' appetite for new revenue opportunities. Many have turned to enterprise data for its enormous potential to drive top-line growth and bottom-line savings. The challenge? Retailers are rich in data but starving for insights.

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CPG Data Analytics | Unlocking Decision Intelligence with CPG Data Insights

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CPG Data Analytics | Unlocking Decision Intelligence with CPG Data Insights

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Leverage the CPG data analytics and AI consulting by Tredence CPG Data  Insights to accelerate cloud data modernization, humanize and adopt  AI to achieve global scalability.

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CPG Data Analytics | Unlocking Decision Intelligence with CPG Data Insights

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Leverage the CPG data analytics and AI consulting by Tredence CPG Data Insights to accelerate cloud data modernization, humanize and adopt AI to achieve global scalability.

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Data Science Solutions Company I AI Solution Provider in USA - Tredence

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Data Science Solutions Company I AI Solution Provider in USA - Tredence

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Tredence is a data science and AI engineering company focused on solving the last mile problem in analytics. The ‘last mile’ is defined as the gap between insight creation and value realization.

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Tredence: An Analytics Services and Solutions Company

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Tredence is an analytics services and solutions company serving some of the leading Fortune 500 clients. We Deliver Business Impact By Enabling Last Mile Adoption Of Insights Through Scalable Analytical Solutions. Tredence has a successful track record of delivering business impact to Fortune 500 cu